The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to MRI systems and methods for simultaneously acquiring images from multiple different slice locations.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically-proven pulse sequences and they also enable the development of new pulse sequences.
The MR signals acquired with an MRI system are signal samples of the subject of the examination in Fourier space, or what is often referred to in the art as “k-space.” Each MR measurement cycle, or pulse sequence, typically samples a portion of k-space along a sampling trajectory characteristic of that pulse sequence. Most pulse sequences sample k-space in a raster scan-like pattern sometimes referred to as a “spin-warp,” a “Fourier,” a “rectilinear,” or a “Cartesian” scan. The spin-warp scan technique employs a variable amplitude phase encoding magnetic field gradient pulse prior to the acquisition of MR spin-echo signals to phase encode spatial information in the direction of this gradient. In a two-dimensional implementation (“2DFT”), for example, spatial information is encoded in one direction by applying a phase encoding gradient, Gy, along that direction, and then a spin-echo signal is acquired in the presence of a readout magnetic field gradient, Gx, in a direction orthogonal to the phase encoding direction. The readout gradient present during the spin-echo acquisition encodes spatial information in the orthogonal direction. In a typical 2DFT pulse sequence, the magnitude of the phase encoding gradient pulse, Gy, is incremented, ΔGy, in the sequence of measurement cycles, or “views” that are acquired during the scan to produce a set of k-space MR data from which an entire image can be reconstructed.
There are many other k-space sampling patterns used by MRI systems. These include “radial”, or “projection reconstruction” scans in which k-space is sampled as a set of radial sampling trajectories extending from the center of k-space. The pulse sequences for a radial scan are characterized by the lack of a phase encoding gradient and the presence of a readout gradient that changes direction from one pulse sequence view to the next. There are also many k-space sampling methods that are closely related to the radial scan and that sample along a curved k-space sampling trajectory rather than the straight line radial trajectory.
An image is reconstructed from the acquired k-space data by transforming the k-space data set to an image space data set. There are many different methods for performing this task and the method used is often determined by the technique used to acquire the k-space data. With a Cartesian grid of k-space data that results from a 2D or 3D spin-warp acquisition, for example, the most common reconstruction method used is an inverse Fourier transformation (“2DFT” or “3DFT”) along each of the 2 or 3 axes of the data set. With a radial k-space data set and its variations, the most common reconstruction method includes “regridding” the k-space samples to create a Cartesian grid of k-space samples and then performing a 2DFT or 3DFT on the regridded k-space data set. In the alternative, a radial k-space data set can also be transformed to Radon space by performing a 1DFT of each radial projection view and then transforming the Radon space data set to image space by performing a filtered backprojection.
Depending on the technique used, many MR scans currently require many minutes to acquire the necessary data used to produce medical images. The reduction of this scan time is an important consideration, since reduced scan time increases patient throughout, improves patient comfort, and improves image quality by reducing motion artifacts. Many different strategies have been developed to shorten the scan time.
One such strategy is referred to generally as “parallel MRI” (“pMRI”). Parallel MRI techniques use spatial information from arrays of radio frequency (“RF”) receiver coils to substitute for the spatial encoding that would otherwise have to be obtained in a sequential fashion using RF pulses and magnetic field gradients, such as phase and frequency encoding gradients. Each of the spatially independent receiver coils of the array carries certain spatial information and has a different spatial sensitivity profile. This information is utilized in order to achieve a complete spatial encoding of the received MR signals, for example, by combining the simultaneously acquired data received from each of the separate coils. Parallel MRI techniques allow an undersampling of k-space by reducing the number of acquired phase-encoded k-space sampling lines, while keeping the maximal extent covered in k-space fixed. The combination of the separate MR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image, in comparison to a conventional k-space data acquisition, by a factor related to the number of the receiver coils. Thus the use of multiple receiver coils acts to multiply imaging speed, without increasing gradient switching rates or RF power.
Two categories of such parallel imaging techniques that have been developed and applied to in vivo imaging are so-called “image space methods” and “k-space methods.” An exemplary image space method is known in the art as sensitivity encoding (“SENSE”), while an exemplary k-space method is known in the art as simultaneous acquisition of spatial harmonics (“SMASH”). With SENSE, the undersampled k-space data is first Fourier transformed to produce an aliased image from each coil, and then the aliased image signals are unfolded by a linear transformation of the superimposed pixel values. With SMASH, the omitted k-space lines are synthesized or reconstructed prior to Fourier transformation, by constructing a weighted combination of neighboring k-space lines acquired by the different receiver coils. SMASH requires that the spatial sensitivity of the coils be determined, and one way to do so is by “autocalibration” that entails the use of variable density k-space sampling.
Advancements to the SMASH techniques include using autocalibration in a technique known as generalized autocalibrating partially parallel acquisitions (“GRAPPA”), as described, for example, in U.S. Pat. No. 6,841,998. With GRAPPA, k-space lines near the center of k-space are sampled at the Nyquist frequency, in comparison to the undersampling employed in the peripheral regions of k-space. These center k-space lines are referred to as the so-called autocalibration signal (“ACS”) lines, which are used to determine the weighting factors that are utilized to synthesize, or reconstruct, the missing k-space lines. In particular, a linear combination of individual coil data is used to create the missing lines of k-space. The coefficients for the combination are determined by fitting the acquired data to the more highly sampled data near the center of k-space.
Thus, conventional parallel MRI techniques rely on accelerating standard image acquisitions by undersampling k-space. These methods undersample k-space by reducing the number of phase-encodings acquired during each repetition of a pulse sequence. The acceleration is typically described in terms of the acceleration factor (“r-factor”). Related to the r-factor is the geometry factor (“g-factor”). For clarity, it is noted that the “r-factor”, or simply R, is the amount of acceleration achieved using a parallel imaging technique. Thus, if the acquisition is sped up by a factor of three, the acquisition achieved an R of 3. The g-factor, however, is a measure of the resulting image quality in terms of the noise that becomes amplified during the application of the reconstruction algorithm (e.g., the SENSE algorithm or the GRAPPA algorithm). Because the value of g varies from pixel to pixel, a map of g-factors can be generated for the image to quantify the level of noise enhancement, and the values of g range from 1.00 to infinity, but are not integer values.
Unfortunately, scan time for volume coverage with echo-planar imaging (“EPI”) or spiral-type MRI data acquisitions has not been substantially decreased by conventional parallel imaging techniques. This lack in scan-time reduction is because, if multiple slices are employed to cover the volume, the time of volume coverage is equal to the product of the number of slices needed to cover the volume and the acquisition period of each slice. The image acquisition period for each slice remains significant even when spatial encoding times are shortened by techniques like parallel imaging. This lack of scan time reduction is especially true when a physiological contrast preparation period (e.g. for imaging neuronal activity or water diffusion) precedes the spatial encoding period for each slice; the former can equal or exceed the latter in rapid imaging sequences such as EPI because it must be repeated for each slice. The problem is the same for fast acquisition techniques such as turbo-spin echo (“TSE”) or fast spin echo (“FSE”); namely, multiple refocused echoes are formed using 180-degree pulses, as opposed to gradient reversal in EPI, to cover multiple k-space lines.
For example, high spatial resolution functional magnetic resonance imaging (“fMRI”) acquisition methods commonly utilize the EPI pulse sequence. The EPI acquisition for each slice can take, for example, about 80 to 100 milliseconds (“ms”). Whole-head coverage at high resolution can require as many as 128 slices for 1 mm isotropic image voxels. Thus, the temporal resolution for the time series is 128 times 80 to 100 ms, which is around 10 seconds. This temporal resolution is often too slow for many functional paradigms, especially event-related paradigms. Thus, at best, an EPI sequence is typically only sped up by around 20 percent when using parallel imaging techniques, whereas conventional imaging sequences are sped up by around 200-300 percent.
Recently, other methods for decreasing scan time have been developed. For example, methods for the simultaneous acquisition of image data from multiple imaging slice locations, using an array of multiple RF receiver coils, and subsequent separation of the superimposed slices during image reconstruction have be introduced, as described by D. J. Larkman, et al., in “Use of Multicoil Arrays for Separation of Signal from Multiple Slices Simultaneously Excited,” Journal of Magnetic Resonance Imaging, 2001; 13(2):313-317.
The Larkman method has been improved upon for conventional image acquisition techniques, as described, for example by F. A. Breuer, et al., in “Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA) for Multi-Slice Imaging,” Magn. Reson. Med., 2005; 53(3):684-691. This method, referred to as “CAIPIRINHA,” increases the distance between aliased pixels by introducing a one-half FOV shift in the images of every other slice. This shift is achieved by modulating the phase of the RF excitation pulse used to acquire every other line of k-space by 180 degrees. In this manner, when the image slices superimpose, every-other slice is shifted by one-half of the FOV. Thus, aliased pixels are separated by one-half the FOV in the phase-encoding direction, and are separated, for example, by the 3 cm distance between the slices in slice-encoding direction. This added separation in the phase-encoding direction improves the ability of parallel image reconstruction methods, such as SENSE, to unalias the slices without producing artifacts or significant noise amplification. In addition, the one-half FOV shift also has the benefit that the method does not completely rely on the distribution in coil sensitivity modulation in the slice-encoding direction.
The CAIPIRINHA method still has challenges in various situations. For example, the one-half FOV shift imparted to every other slice is achieved by modulating the phase of the RF excitation pulses of every other line in k-space. While this is applicable to conventional acquisitions, in which every line of k-space is acquired with a separate excitation, it is not applicable to EPI acquisitions, in which all the lines of k-space are acquired after a single RF excitation. Thus, the CAIPIRINHA method is generally not extendable to EPI acquisitions.
Additionally, methods for simultaneous multi-slice imaging have been described, for example, by D. A. Feinberg, et al., in “Simultaneous Echo Refocusing in EPI,” Magn. Reson. Med., 2002; 48(1):1-5. In such methods, which may be referred to as “SER-EPI,” the RF excitation of the slices is sequential, as opposed to truly simultaneous. A readout gradient pulse is applied between two sequential excitations, and acts to shift the k-space data of one slice relative to the other along the kx-direction, which corresponds to the readout direction in image space. By lengthening the readout window, the k-space data for both slices is captured sequentially. The data can then be cut apart and reconstructed separately.
CAIPIRHINA and other simultaneous multi-slice methods have not yet gained great traction in conventional imaging, since there are alternative parallel imaging methods, such as conventional SENSE and GRAPPA, for accelerating standard image acquisitions. However, as noted above, these methods do not confer the same acceleration benefits on pulse sequences such as EPI as they do on other conventional pulse sequences. Unlike parallel imaging methods such as SENSE and GRAPPA, multi-slice acquisition techniques do not aim to shorten the time spent on reading out k-space data, for example, by reducing the number of phase-encodings. Rather, they aim to acquire signal data from multiple image slice locations per acquisition, such that the number of repetitions of a pulse sequence can be reduced to similarly reduce overall scan time. For example, a three-fold accelerated multi-slice acquisition acquires image data from three image slice locations per each repetition of the EPI sequence. As a result of this simultaneous acquisition, the number of repetitions of an EPI sequence required to cover an imaging volume is reduced, thereby similarly reducing the total acquisition time.
While such techniques allow for single-shot EPI acquisitions that mitigate the distortion and blurring effects seen at high magnetic field strengths, there is an intrinsic signal-to-noise ratio (“SNR”) penalty that scales with the square-root of the r-factor and with g-factor, ultimately limiting its usability. Multi-shot segmented EPI acquisitions can similarly mitigate these deleterious effects, yet the longer temporal sampling interval amplifies physiological noise and system instabilities.
Thus, there continues to be a desire for systems and methods for MR imaging that yield both the inherent flexibility and benefits of EPI, but with substantial acceleration together with high image SNR and low physiological noise levels and low system instability.